Stochastic discrimination (SD) is a methodology for creating classifiers for use in, for example, pattern recognition. It is based on serially combining arbitrary numbers of weak components, which are usually generated by some pseudorandom process, to generalize to new data. For an implementation of SD training, please see E. M. Kleinberg, “On the algorithmic implementation of stochastic discrimination,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 5, May 2000, 473-490.